ENT MERFISH Report
1. Overview
1.1 Sample Information
A brief sample information is generated from the submission table for the following analysis.
| Expt | Sample | Index | Genotype | Group | Region | DataPath |
|---|---|---|---|---|---|---|
| 1 | NT28530x2 | NT | NT | Normal | region_0 | Y:_Imaging_data_2\202409271426_20240927ENT28519ImmunOnc500VA067x01_VMSC00101 |
| 1 | TT28519x1 | TT | TT | Tumor | region_1 | Y:_Imaging_data_2\202409271426_20240927ENT28519ImmunOnc500VA067x01_VMSC00101 |
| 1 | TT28519x2 | TT_dupe | TT | Tumor | region_2 | Y:_Imaging_data_2\202409271426_20240927ENT28519ImmunOnc500VA067x01_VMSC00101 |
| 1 | NT28530x1 | NT_dupe | NT | Normal | region_3 | Y:_Imaging_data_2\202409271426_20240927ENT28519ImmunOnc500VA067x01_VMSC00101 |
1.2 MERSCOPE Data Quality Summary
The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.
Generally, log10 transcript count > 4.0 in most area can be considered as a good quality standard.
Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.
1.2.1 NT28530x2, (Normal)
1.2.2 TT28519x1, (Tumor)
1.2.3 TT28519x2, (Tumor )
1.2.4 NT28530x1, (Normal)
1.3 Transcript Mis-Match on FOV Boundary
2. Data Processing & Analysis
2.1 Cell Segmentation & Filtering
Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.
To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 2500], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.
Outliers were filtered from the data, and the qualified cell count is presented below. The transcript count Violin and transcript count Spatial Map are displayed here as part of the quality control reveal.
2.1.1 Cell Count after Filtering
2.1.2 Transcript Count Violin
Transcript Count Violin After Filtering
2.1.3 Transcript Count Spatial Map
Transcript Count Spatial Map After Filtering
2.2 Batch Effect & Dimension Reduction
We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.
Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.
Umap of cells and colored by batch
3. (will do) Cell Annotation
4. (will do) Differential Analysis
Supplement: Abbreviation
Cell types & Regions
Astro, Astrocyte;
ABC, arachnoid barrier cells;
BAM, border-associated macrophages;
BLA, Basolateral amygdala;
CB, cerebellum;
CGE, caudal ganglionic eminence;
CHOR, choroid plexus;
CNU, cerebral nuclei;
CR, Cajal–Retzius;
CT, corticothalamic;
CTX, cerebral cortex;
CTXsp, cortical subplate;
DC, dendritic cells;
DCO, dorsal cochlear nucleus;
DG, dentate gyrus;
EA, extended amygdala;
Endo, endothelial cells;
ENT, Entorhinal area;
ENTl, Entorhinal area, lateral part;
Epen, ependymal;
EPI, epithalamus;
ET, extratelencephalic;
GC, granule cell;
HB, hindbrain;
HPF, hippocampal formation;
HY, hypothalamus;
HYa, anterior hypothalamic;
IMN, immature neurons;
IT, intratelencephalic;
L6b, layer 6b;
LGE, lateral ganglionic eminence;
LH, lateral habenula;
LSX, lateral septal complex;
MB, midbrain;
MGE, medial ganglionic eminence;
MH, medial habenula;
MM, medial mammillary nucleus;
MY, medulla;
NN, non-neuronal;
NP, near-projecting;
NT, non-telencephalon;
OB, olfactory bulb;
OEC, olfactory ensheathing cells;
OLF, olfactory areas;
Oligo, oligodendrocytes;
OPC, oligodendrocyte precursor cells;
P, pons;
PAL, pallidum;
Peri, pericytes;
PIR, piriform cortex;
SMC, smooth muscle cells;
STR, striatum;
TE, telencephalon;
TH, thalamus;
UBC, unipolar brush cells;
VLMC, vascular leptomeningeal cells.
Neurotransmitter types
Chol, cholinergic;
Dopa, dopaminergic;
GABA, GABAergic;
Glut, glutamatergic;
Glyc, glycinergic;
Hist, histaminergic;
Nora, noradrenergic;
Sero, serotonergic
ADP, anterodorsal preoptic nucleus
AHN, anterior hypothalamic nucleus
ARH, arcuate hypothalamic nucleus
CLI, central linear nucleus raphe
CUN, cuneiform nucleus
DMH, dorsomedial nucleus of the hypothalamus
DMX, dorsal motor nucleus of the vagus nerve
IF, interfascicular nucleus raphe
LHA, lateral hypothalamic area
MDRN, medullary reticular nucleus
MPN, medial preoptic nucleus
MPO, medial preoptic area
MV, medial vestibular nucleus
NTS, nucleus of the solitary tract
PAG, periaqueductal grey
PARN, parvicellular reticular nucleus
PB, parabrachial nucleus
PBG, parabigeminal nucleus
PGRN, paragigantocellular reticular nucleus
PGRNd, paragigantocellular reticular nucleus, dorsal part
PH, posterior hypothalamic nucleus
PMv, ventral premammillary nucleus
PPN, pedunculopontine nucleus
PVa, periventricular hypothalamic nucleus, anterior part
PVHd, paraventricular hypothalamic nucleus, descending division
PVi, periventricular hypothalamic nucleus, intermediate part
PVpo, periventricular hypothalamic nucleus, preoptic part
PVR, periventricular region
RAmb, midbrain raphe nuclei
RL, rostral linear nucleus raphe
SBPV, subparaventricular zone
SNc, substantia nigra, compact part
SPIV, spinal vestibular nucleus
TMv, tuberomammillary nucleus, ventral part
VII, facial motor nucleus
VMPO, ventromedial preoptic nucleus
VTA, ventral tegmental area
ZI, zona incerta.